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<div class="header">
  <div class="summary">
<a href="#func-members">Functions</a>  </div>
  <div class="headertitle">
<div class="title">The module brings implementations of intensity transformation algorithms to adjust image contrast.</div>  </div>
</div><!--header-->
<div class="contents">
<table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="func-members"></a>
Functions</h2></td></tr>
<tr class="memitem:ga8cc227ca2e5918620786ce44b93270e5"><td align="right" class="memItemLeft" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="../../dc/dfe/group__intensity__transform.html#ga8cc227ca2e5918620786ce44b93270e5">cv::intensity_transform::autoscaling</a> (const <a class="el" href="../../d3/d63/classcv_1_1Mat.html">Mat</a> input, <a class="el" href="../../d3/d63/classcv_1_1Mat.html">Mat</a> &amp;output)</td></tr>
<tr class="memdesc:ga8cc227ca2e5918620786ce44b93270e5"><td class="mdescLeft"> </td><td class="mdescRight">Given an input bgr or grayscale image, apply autoscaling on domain [0, 255] to increase the contrast of the input image and return the resulting image.  <a href="../../dc/dfe/group__intensity__transform.html#ga8cc227ca2e5918620786ce44b93270e5">More...</a><br/></td></tr>
<tr class="separator:ga8cc227ca2e5918620786ce44b93270e5"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ga9b65943a38b905f9ed42a622de92e740"><td align="right" class="memItemLeft" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="../../dc/dfe/group__intensity__transform.html#ga9b65943a38b905f9ed42a622de92e740">cv::intensity_transform::BIMEF</a> (<a class="el" href="../../dc/d84/group__core__basic.html#ga353a9de602fe76c709e12074a6f362ba">InputArray</a> input, <a class="el" href="../../dc/d84/group__core__basic.html#gaad17fda1d0f0d1ee069aebb1df2913c0">OutputArray</a> output, float mu=0.5f, float a=-0.3293f, float b=1.1258f)</td></tr>
<tr class="memdesc:ga9b65943a38b905f9ed42a622de92e740"><td class="mdescLeft"> </td><td class="mdescRight">Given an input color image, enhance low-light images using the BIMEF method (<a class="el" href="../../d0/de3/citelist.html#CITEREF_ying2017bio">[284]</a> <a class="el" href="../../d0/de3/citelist.html#CITEREF_ying2017new">[285]</a>).  <a href="../../dc/dfe/group__intensity__transform.html#ga9b65943a38b905f9ed42a622de92e740">More...</a><br/></td></tr>
<tr class="separator:ga9b65943a38b905f9ed42a622de92e740"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:gadae1a95a11d75a9c22444844e712d8b8"><td align="right" class="memItemLeft" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="../../dc/dfe/group__intensity__transform.html#gadae1a95a11d75a9c22444844e712d8b8">cv::intensity_transform::BIMEF</a> (<a class="el" href="../../dc/d84/group__core__basic.html#ga353a9de602fe76c709e12074a6f362ba">InputArray</a> input, <a class="el" href="../../dc/d84/group__core__basic.html#gaad17fda1d0f0d1ee069aebb1df2913c0">OutputArray</a> output, float k, float mu, float a, float b)</td></tr>
<tr class="memdesc:gadae1a95a11d75a9c22444844e712d8b8"><td class="mdescLeft"> </td><td class="mdescRight">Given an input color image, enhance low-light images using the BIMEF method (<a class="el" href="../../d0/de3/citelist.html#CITEREF_ying2017bio">[284]</a> <a class="el" href="../../d0/de3/citelist.html#CITEREF_ying2017new">[285]</a>).  <a href="../../dc/dfe/group__intensity__transform.html#gadae1a95a11d75a9c22444844e712d8b8">More...</a><br/></td></tr>
<tr class="separator:gadae1a95a11d75a9c22444844e712d8b8"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:gaabdbfa8715c8c813f4c862986b1a0882"><td align="right" class="memItemLeft" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="../../dc/dfe/group__intensity__transform.html#gaabdbfa8715c8c813f4c862986b1a0882">cv::intensity_transform::contrastStretching</a> (const <a class="el" href="../../d3/d63/classcv_1_1Mat.html">Mat</a> input, <a class="el" href="../../d3/d63/classcv_1_1Mat.html">Mat</a> &amp;output, const int r1, const int s1, const int r2, const int s2)</td></tr>
<tr class="memdesc:gaabdbfa8715c8c813f4c862986b1a0882"><td class="mdescLeft"> </td><td class="mdescRight">Given an input bgr or grayscale image, apply linear contrast stretching on domain [0, 255] and return the resulting image.  <a href="../../dc/dfe/group__intensity__transform.html#gaabdbfa8715c8c813f4c862986b1a0882">More...</a><br/></td></tr>
<tr class="separator:gaabdbfa8715c8c813f4c862986b1a0882"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:gaec85454c2cd29f2440fefd55d609e682"><td align="right" class="memItemLeft" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="../../dc/dfe/group__intensity__transform.html#gaec85454c2cd29f2440fefd55d609e682">cv::intensity_transform::gammaCorrection</a> (const <a class="el" href="../../d3/d63/classcv_1_1Mat.html">Mat</a> input, <a class="el" href="../../d3/d63/classcv_1_1Mat.html">Mat</a> &amp;output, const float gamma)</td></tr>
<tr class="memdesc:gaec85454c2cd29f2440fefd55d609e682"><td class="mdescLeft"> </td><td class="mdescRight">Given an input bgr or grayscale image and constant gamma, apply power-law transformation, a.k.a. gamma correction to the image on domain [0, 255] and return the resulting image.  <a href="../../dc/dfe/group__intensity__transform.html#gaec85454c2cd29f2440fefd55d609e682">More...</a><br/></td></tr>
<tr class="separator:gaec85454c2cd29f2440fefd55d609e682"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:gab480ce8b4bbd00195f66d9dec1d5e4ad"><td align="right" class="memItemLeft" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="../../dc/dfe/group__intensity__transform.html#gab480ce8b4bbd00195f66d9dec1d5e4ad">cv::intensity_transform::logTransform</a> (const <a class="el" href="../../d3/d63/classcv_1_1Mat.html">Mat</a> input, <a class="el" href="../../d3/d63/classcv_1_1Mat.html">Mat</a> &amp;output)</td></tr>
<tr class="memdesc:gab480ce8b4bbd00195f66d9dec1d5e4ad"><td class="mdescLeft"> </td><td class="mdescRight">Given an input bgr or grayscale image and constant c, apply log transformation to the image on domain [0, 255] and return the resulting image.  <a href="../../dc/dfe/group__intensity__transform.html#gab480ce8b4bbd00195f66d9dec1d5e4ad">More...</a><br/></td></tr>
<tr class="separator:gab480ce8b4bbd00195f66d9dec1d5e4ad"><td class="memSeparator" colspan="2"> </td></tr>
</table>
<a id="details" name="details"></a><h2 class="groupheader">Detailed Description</h2>
<p>Namespace for all functions is <code><a class="el" href="../../db/d4d/namespacecv_1_1intensity__transform.html">cv::intensity_transform</a></code>.</p>
<h3>Supported Algorithms</h3>
<ul>
<li>Autoscaling</li>
<li>Log Transformations</li>
<li>Power-Law (Gamma) Transformations</li>
<li>Contrast Stretching</li>
<li>BIMEF, A Bio-Inspired Multi-Exposure Fusion Framework for Low-light Image Enhancement <a class="el" href="../../d0/de3/citelist.html#CITEREF_ying2017bio">[284]</a> <a class="el" href="../../d0/de3/citelist.html#CITEREF_ying2017new">[285]</a></li>
</ul>
<p>References from following book and websites:</p><ul>
<li>Digital Image Processing 4th Edition Chapter 3 [Rafael C. Gonzalez, Richard E. Woods] <a class="el" href="../../d0/de3/citelist.html#CITEREF_Gonzalez2018">[200]</a></li>
<li><a href="http://www.cs.uregina.ca/Links/class-info/425/Lab3/">http://www.cs.uregina.ca/Links/class-info/425/Lab3/</a> <a class="el" href="../../d0/de3/citelist.html#CITEREF_lcs435lab">[135]</a></li>
<li><a href="https://theailearner.com/2019/01/30/contrast-stretching/">https://theailearner.com/2019/01/30/contrast-stretching/</a> <a class="el" href="../../d0/de3/citelist.html#CITEREF_theailearner">[245]</a> </li>
</ul>
<h2 class="groupheader">Function Documentation</h2>
<a id="ga8cc227ca2e5918620786ce44b93270e5"></a>
<h2 class="memtitle"><span class="permalink"><a href="#ga8cc227ca2e5918620786ce44b93270e5">◆ </a></span>autoscaling()</h2>
<div class="memitem">
<div class="memproto">
      <table class="memname">
        <tr>
          <td class="memname">void cv::intensity_transform::autoscaling </td>
          <td>(</td>
          <td class="paramtype">const <a class="el" href="../../d3/d63/classcv_1_1Mat.html">Mat</a> </td>
          <td class="paramname"><em>input</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype"><a class="el" href="../../d3/d63/classcv_1_1Mat.html">Mat</a> &amp; </td>
          <td class="paramname"><em>output</em> </td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td></td>
        </tr>
      </table><table class="python_language"><tr><th colspan="999" style="text-align:left">Python:</th></tr><tr><td style="width: 20px;"></td><td>None</td><td>=</td><td>cv.intensity_transform.autoscaling(</td><td class="paramname">input, output</td><td>)</td></tr></table>
</div><div class="memdoc">
<p><code>#include &lt;<a class="el" href="../../da/d3c/intensity__transform_8hpp.html">opencv2/intensity_transform.hpp</a>&gt;</code></p>
<p>Given an input bgr or grayscale image, apply autoscaling on domain [0, 255] to increase the contrast of the input image and return the resulting image. </p>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">input</td><td>input bgr or grayscale image. </td></tr>
    <tr><td class="paramname">output</td><td>resulting image of autoscaling. </td></tr>
  </table>
  </dd>
</dl>
</div>
</div>
<a id="ga9b65943a38b905f9ed42a622de92e740"></a>
<h2 class="memtitle"><span class="permalink"><a href="#ga9b65943a38b905f9ed42a622de92e740">◆ </a></span>BIMEF() <span class="overload">[1/2]</span></h2>
<div class="memitem">
<div class="memproto">
      <table class="memname">
        <tr>
          <td class="memname">void cv::intensity_transform::BIMEF </td>
          <td>(</td>
          <td class="paramtype"><a class="el" href="../../dc/d84/group__core__basic.html#ga353a9de602fe76c709e12074a6f362ba">InputArray</a> </td>
          <td class="paramname"><em>input</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype"><a class="el" href="../../dc/d84/group__core__basic.html#gaad17fda1d0f0d1ee069aebb1df2913c0">OutputArray</a> </td>
          <td class="paramname"><em>output</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">float </td>
          <td class="paramname"><em>mu</em> = <code>0.5f</code>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">float </td>
          <td class="paramname"><em>a</em> = <code>-0.3293f</code>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">float </td>
          <td class="paramname"><em>b</em> = <code>1.1258f</code> </td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td></td>
        </tr>
      </table><table class="python_language"><tr><th colspan="999" style="text-align:left">Python:</th></tr><tr><td style="width: 20px;"></td><td>output</td><td>=</td><td>cv.intensity_transform.BIMEF(</td><td class="paramname">input[, output[, mu[, a[, b]]]]</td><td>)</td></tr><tr><td style="width: 20px;"></td><td>output</td><td>=</td><td>cv.intensity_transform.BIMEF2(</td><td class="paramname">input, k, mu, a, b[, output]</td><td>)</td></tr></table>
</div><div class="memdoc">
<p><code>#include &lt;<a class="el" href="../../da/d3c/intensity__transform_8hpp.html">opencv2/intensity_transform.hpp</a>&gt;</code></p>
<p>Given an input color image, enhance low-light images using the BIMEF method (<a class="el" href="../../d0/de3/citelist.html#CITEREF_ying2017bio">[284]</a> <a class="el" href="../../d0/de3/citelist.html#CITEREF_ying2017new">[285]</a>). </p>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">input</td><td>input color image. </td></tr>
    <tr><td class="paramname">output</td><td>resulting image. </td></tr>
    <tr><td class="paramname">mu</td><td>enhancement ratio. </td></tr>
    <tr><td class="paramname">a</td><td>a-parameter in the Camera Response Function (CRF). </td></tr>
    <tr><td class="paramname">b</td><td>b-parameter in the Camera Response Function (CRF).</td></tr>
  </table>
  </dd>
</dl>
<dl class="section warning"><dt>Warning</dt><dd>This is a C++ implementation of the <a href="https://github.com/baidut/BIMEF">original MATLAB algorithm</a>. Compared to the original code, this implementation is a little bit slower and does not provide the same results. In particular, quality of the image enhancement is degraded for the bright areas in certain conditions. </dd></dl>
</div>
</div>
<a id="gadae1a95a11d75a9c22444844e712d8b8"></a>
<h2 class="memtitle"><span class="permalink"><a href="#gadae1a95a11d75a9c22444844e712d8b8">◆ </a></span>BIMEF() <span class="overload">[2/2]</span></h2>
<div class="memitem">
<div class="memproto">
      <table class="memname">
        <tr>
          <td class="memname">void cv::intensity_transform::BIMEF </td>
          <td>(</td>
          <td class="paramtype"><a class="el" href="../../dc/d84/group__core__basic.html#ga353a9de602fe76c709e12074a6f362ba">InputArray</a> </td>
          <td class="paramname"><em>input</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype"><a class="el" href="../../dc/d84/group__core__basic.html#gaad17fda1d0f0d1ee069aebb1df2913c0">OutputArray</a> </td>
          <td class="paramname"><em>output</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">float </td>
          <td class="paramname"><em>k</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">float </td>
          <td class="paramname"><em>mu</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">float </td>
          <td class="paramname"><em>a</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">float </td>
          <td class="paramname"><em>b</em> </td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td></td>
        </tr>
      </table><table class="python_language"><tr><th colspan="999" style="text-align:left">Python:</th></tr><tr><td style="width: 20px;"></td><td>output</td><td>=</td><td>cv.intensity_transform.BIMEF(</td><td class="paramname">input[, output[, mu[, a[, b]]]]</td><td>)</td></tr><tr><td style="width: 20px;"></td><td>output</td><td>=</td><td>cv.intensity_transform.BIMEF2(</td><td class="paramname">input, k, mu, a, b[, output]</td><td>)</td></tr></table>
</div><div class="memdoc">
<p><code>#include &lt;<a class="el" href="../../da/d3c/intensity__transform_8hpp.html">opencv2/intensity_transform.hpp</a>&gt;</code></p>
<p>Given an input color image, enhance low-light images using the BIMEF method (<a class="el" href="../../d0/de3/citelist.html#CITEREF_ying2017bio">[284]</a> <a class="el" href="../../d0/de3/citelist.html#CITEREF_ying2017new">[285]</a>). </p>
<p>This is an overloaded function with the exposure ratio given as parameter.</p>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">input</td><td>input color image. </td></tr>
    <tr><td class="paramname">output</td><td>resulting image. </td></tr>
    <tr><td class="paramname">k</td><td>exposure ratio. </td></tr>
    <tr><td class="paramname">mu</td><td>enhancement ratio. </td></tr>
    <tr><td class="paramname">a</td><td>a-parameter in the Camera Response Function (CRF). </td></tr>
    <tr><td class="paramname">b</td><td>b-parameter in the Camera Response Function (CRF).</td></tr>
  </table>
  </dd>
</dl>
<dl class="section warning"><dt>Warning</dt><dd>This is a C++ implementation of the <a href="https://github.com/baidut/BIMEF">original MATLAB algorithm</a>. Compared to the original code, this implementation is a little bit slower and does not provide the same results. In particular, quality of the image enhancement is degraded for the bright areas in certain conditions. </dd></dl>
</div>
</div>
<a id="gaabdbfa8715c8c813f4c862986b1a0882"></a>
<h2 class="memtitle"><span class="permalink"><a href="#gaabdbfa8715c8c813f4c862986b1a0882">◆ </a></span>contrastStretching()</h2>
<div class="memitem">
<div class="memproto">
      <table class="memname">
        <tr>
          <td class="memname">void cv::intensity_transform::contrastStretching </td>
          <td>(</td>
          <td class="paramtype">const <a class="el" href="../../d3/d63/classcv_1_1Mat.html">Mat</a> </td>
          <td class="paramname"><em>input</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype"><a class="el" href="../../d3/d63/classcv_1_1Mat.html">Mat</a> &amp; </td>
          <td class="paramname"><em>output</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const int </td>
          <td class="paramname"><em>r1</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const int </td>
          <td class="paramname"><em>s1</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const int </td>
          <td class="paramname"><em>r2</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const int </td>
          <td class="paramname"><em>s2</em> </td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td></td>
        </tr>
      </table><table class="python_language"><tr><th colspan="999" style="text-align:left">Python:</th></tr><tr><td style="width: 20px;"></td><td>None</td><td>=</td><td>cv.intensity_transform.contrastStretching(</td><td class="paramname">input, output, r1, s1, r2, s2</td><td>)</td></tr></table>
</div><div class="memdoc">
<p><code>#include &lt;<a class="el" href="../../da/d3c/intensity__transform_8hpp.html">opencv2/intensity_transform.hpp</a>&gt;</code></p>
<p>Given an input bgr or grayscale image, apply linear contrast stretching on domain [0, 255] and return the resulting image. </p>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">input</td><td>input bgr or grayscale image. </td></tr>
    <tr><td class="paramname">output</td><td>resulting image of contrast stretching. </td></tr>
    <tr><td class="paramname">r1</td><td>x coordinate of first point (r1, s1) in the transformation function. </td></tr>
    <tr><td class="paramname">s1</td><td>y coordinate of first point (r1, s1) in the transformation function. </td></tr>
    <tr><td class="paramname">r2</td><td>x coordinate of second point (r2, s2) in the transformation function. </td></tr>
    <tr><td class="paramname">s2</td><td>y coordinate of second point (r2, s2) in the transformation function. </td></tr>
  </table>
  </dd>
</dl>
</div>
</div>
<a id="gaec85454c2cd29f2440fefd55d609e682"></a>
<h2 class="memtitle"><span class="permalink"><a href="#gaec85454c2cd29f2440fefd55d609e682">◆ </a></span>gammaCorrection()</h2>
<div class="memitem">
<div class="memproto">
      <table class="memname">
        <tr>
          <td class="memname">void cv::intensity_transform::gammaCorrection </td>
          <td>(</td>
          <td class="paramtype">const <a class="el" href="../../d3/d63/classcv_1_1Mat.html">Mat</a> </td>
          <td class="paramname"><em>input</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype"><a class="el" href="../../d3/d63/classcv_1_1Mat.html">Mat</a> &amp; </td>
          <td class="paramname"><em>output</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const float </td>
          <td class="paramname"><em>gamma</em> </td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td></td>
        </tr>
      </table><table class="python_language"><tr><th colspan="999" style="text-align:left">Python:</th></tr><tr><td style="width: 20px;"></td><td>None</td><td>=</td><td>cv.intensity_transform.gammaCorrection(</td><td class="paramname">input, output, gamma</td><td>)</td></tr></table>
</div><div class="memdoc">
<p><code>#include &lt;<a class="el" href="../../da/d3c/intensity__transform_8hpp.html">opencv2/intensity_transform.hpp</a>&gt;</code></p>
<p>Given an input bgr or grayscale image and constant gamma, apply power-law transformation, a.k.a. gamma correction to the image on domain [0, 255] and return the resulting image. </p>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">input</td><td>input bgr or grayscale image. </td></tr>
    <tr><td class="paramname">output</td><td>resulting image of gamma corrections. </td></tr>
    <tr><td class="paramname">gamma</td><td>constant in c*r^gamma where r is pixel value. </td></tr>
  </table>
  </dd>
</dl>
</div>
</div>
<a id="gab480ce8b4bbd00195f66d9dec1d5e4ad"></a>
<h2 class="memtitle"><span class="permalink"><a href="#gab480ce8b4bbd00195f66d9dec1d5e4ad">◆ </a></span>logTransform()</h2>
<div class="memitem">
<div class="memproto">
      <table class="memname">
        <tr>
          <td class="memname">void cv::intensity_transform::logTransform </td>
          <td>(</td>
          <td class="paramtype">const <a class="el" href="../../d3/d63/classcv_1_1Mat.html">Mat</a> </td>
          <td class="paramname"><em>input</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype"><a class="el" href="../../d3/d63/classcv_1_1Mat.html">Mat</a> &amp; </td>
          <td class="paramname"><em>output</em> </td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td></td>
        </tr>
      </table><table class="python_language"><tr><th colspan="999" style="text-align:left">Python:</th></tr><tr><td style="width: 20px;"></td><td>None</td><td>=</td><td>cv.intensity_transform.logTransform(</td><td class="paramname">input, output</td><td>)</td></tr></table>
</div><div class="memdoc">
<p><code>#include &lt;<a class="el" href="../../da/d3c/intensity__transform_8hpp.html">opencv2/intensity_transform.hpp</a>&gt;</code></p>
<p>Given an input bgr or grayscale image and constant c, apply log transformation to the image on domain [0, 255] and return the resulting image. </p>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">input</td><td>input bgr or grayscale image. </td></tr>
    <tr><td class="paramname">output</td><td>resulting image of log transformations. </td></tr>
  </table>
  </dd>
</dl>
</div>
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